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This paper considers the problem of partially observed optimal control forforward stochastic systems which are driven by Brownian motions and anindependent Poisson random measure with a feature that the cost functional isof mean-field type. When all the system coefficients and the objectiveperformance functionals are allowed to be random, possibly non-Markovian,Malliavin calculus is employed to derive a maximum principle for the optimalcontrol of such a system where the adjointprocess is explicitly expressed. Wealso investigate the mean-field type optimal control problems for systemsdriven by mean-field type stochastic differential equations (SDEs in short)with jump processes, in which the coefficients contain not only the stateprocess but also its marginal distribution under partially observedinformation. The maximum principle is established using convex variationaltechnique with an illustrating example about linear-quadratic optimal control.

On optimal mean-field type control problems of stochastic systems with jump processes under partial information